Divide classes into truncated and non-truncated objects
It could work. I think itll be hard to find labelled bounding box data describing full/truncated cars so this is a good idea for self labelling.

Theres probably also some other ways for you to get labelled data without the crowd sourcing or self labelling. Take a large labelled car dataset with bounding boxes (COCO has some, and if your good with aerial imagery theres alot more out there), and just enforce that any car bounding boxes that are touching the boundary are truncated, while ones fully encompassed in the imaged are not, since with high probability that will be the case. 

Good luck!